{"id":66240,"date":"2024-12-11T10:29:31","date_gmt":"2024-12-11T10:29:31","guid":{"rendered":"https:\/\/www.sardinasystems.com\/sem-categoria\/fishos-powered-machine-learning-cloud-for-successful-scientific-research\/"},"modified":"2025-05-14T16:16:31","modified_gmt":"2025-05-14T16:16:31","slug":"fishos-powered-machine-learning-cloud-for-successful-scientific-research","status":"publish","type":"post","link":"https:\/\/www.sardinasystems.com\/pt-pt\/o-nosso-blog\/fishos-powered-machine-learning-cloud-for-successful-scientific-research\/","title":{"rendered":"FishOS-Powered Machine Learning Cloud para Investiga\u00e7\u00e3o Cient\u00edfica Bem-Sucedida"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Durante quase dois anos de estreita colabora\u00e7\u00e3o com <strong>um dos principais centros de investiga\u00e7\u00e3o da Alemanha, a Universidade de T\u00fcbingen<\/strong>, a Sardina Systems forneceu suporte essencial para melhorar a efici\u00eancia e produtividade da infraestrutura de nuvem de machine learning da universidade. <br\/>Neste estudo de caso, vamos explorar como<strong> o FishOS, a plataforma de gest\u00e3o de nuvem totalmente automatizada, escal\u00e1vel e econ\u00f3mica da Sardina Systems<\/strong>, ajuda uma pequena equipa de especialistas a gerir as suas tarefas di\u00e1rias, permitindo que os cientistas de dados concentrem os seus esfor\u00e7os na realiza\u00e7\u00e3o de investiga\u00e7\u00f5es inovadoras em \u00e1reas como medicina, agricultura, condu\u00e7\u00e3o aut\u00f3noma e outras, e na entrega de solu\u00e7\u00f5es transformadoras.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Conhecendo a Nuvem de ML<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\"><a href=\"https:\/\/uni-tuebingen.de\/en\/\" target=\"_blank\" rel=\"noopener\" title=\"A Nuvem de ML na Universidade de T&#xFC;bingen\">A Nuvem de ML na Universidade de T\u00fcbingen<\/a>,<\/mark><\/strong> que inclui o Centro de IA e o Cluster de Excel\u00eancia de ML, impulsiona projetos impactantes<strong> em imagiologia m\u00e9dica, incluindo estudos sobre cancro e c\u00e9rebro<\/strong>, bem como <strong>vis\u00e3o computacional e an\u00e1lise de movimento humano<\/strong>, iniciativas de simula\u00e7\u00e3o e previs\u00e3o clim\u00e1tica, e at\u00e9 mesmo o treino de <strong>um rob\u00f4 da Boston Dynamics para tarefas agr\u00edcolas<\/strong>. Com a participa\u00e7\u00e3o de <strong>48 grupos de investiga\u00e7\u00e3o<\/strong>, estes projetos est\u00e3o a ultrapassar fronteiras e <strong>a fazer contribui\u00e7\u00f5es significativas<\/strong> em v\u00e1rias \u00e1reas. <\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png\" alt=\"Esquema da Nuvem de Machine Learning\" class=\"wp-image-66212\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-2-projects-2-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\">Atualmente, recursos humanos limitados est\u00e3o envolvidos na manuten\u00e7\u00e3o de <strong>um cluster composto por cerca de 200 n\u00facleos<\/strong>, respons\u00e1vel por todas as opera\u00e7\u00f5es de dados. Vamos explorar os desafios que os especialistas enfrentaram antes de implementar o FishOS, e como a plataforma os ajudou a superar essas dificuldades. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Abra\u00e7ando o Desafio<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">A equipa de TI da universidade enfrenta desafios distintos inerentes \u00e0s organiza\u00e7\u00f5es p\u00fablicas. Operando dentro de restri\u00e7\u00f5es rigorosas de financiamento governamental, devem garantir <strong>a opera\u00e7\u00e3o cont\u00ednua de um sistema de computa\u00e7\u00e3o cient\u00edfica em v\u00e1rias unidades <\/strong>enquanto tamb\u00e9m atendem<strong> \u00e0s diversas necessidades de computa\u00e7\u00e3o de v\u00e1rios grupos de investiga\u00e7\u00e3o. <\/strong>A estabilidade \u00e9 crucial para os investigadores que procuram resultados precisos, especialmente em \u00e1reas como a investiga\u00e7\u00e3o sobre cancro e c\u00e9rebro, onde o poder de computa\u00e7\u00e3o \u00e9 indispens\u00e1vel para aplica\u00e7\u00f5es de machine learning. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Navegando pelos Obst\u00e1culos<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">Em resposta a esses desafios, <strong>a equipa da universidade optou por aproveitar a infraestrutura local <\/strong>para maximizar a efici\u00eancia do or\u00e7amento e cumprir os requisitos de capacidade. Desenvolveram <strong>uma arquitetura de hardware adaptada para atender \u00e0s diversas necessidades <\/strong>de pequenos grupos de investiga\u00e7\u00e3o dispersos por toda a Alemanha. <strong>Esta arquitetura combina virtualiza\u00e7\u00e3o e metal nu de forma equilibrada<\/strong>, complementada por uma instala\u00e7\u00e3o de <strong>armazenamento Ceph distribu\u00eddo<\/strong> para fins de arquivo. <\/p>\n\n<p class=\"wp-block-paragraph\"><\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png\" alt=\"Vis&#xE3;o Geral da Infraestrutura da Nuvem de Machine Learning\" class=\"wp-image-66221\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-3-system-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\">Aproximadamente <strong>15-20% dos n\u00f3s do sistema s\u00e3o alocados para balanceadores de carga<\/strong>, com os n\u00f3s restantes dedicados ao poder de computa\u00e7\u00e3o. Os hipervisores est\u00e3o equipados com configura\u00e7\u00f5es de <strong>apenas CPU, GPU V100 e GPU A100<\/strong>, com aproximadamente <strong>70% dos n\u00f3s atualmente utilizados atrav\u00e9s de metal nu.<\/strong> Os recursos de GPU, localizados tanto em hipervisores quanto em metal nu, s\u00e3o priorizados<strong> para melhorar a capacidade de resposta do sistema<\/strong>. Al\u00e9m disso, o sistema acomoda tarefas mais convencionais, fornecendo CPUs cl\u00e1ssicas para fun\u00e7\u00f5es como gest\u00e3o de conjuntos de dados.  <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">FishOS: Suportando Infraestrutura Est\u00e1vel e Simplificando Opera\u00e7\u00f5es<\/mark><\/strong><\/p>\n\n<ul class=\"wp-block-list\">\n<li><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\"><em>Interface Unificada.<\/em> <\/mark>Com o FishOS, a gest\u00e3o da infraestrutura da Nuvem de ML, espalhada por v\u00e1rias \u00e1reas, atrav\u00e9s de uma interface unificada tornou-se notavelmente simplificada.<\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Flexibilidade.<\/mark><\/em>  Talvez uma das vantagens mais not\u00e1veis da Nuvem de ML seja a versatilidade do FishOS &#8211; pode funcionar tanto em n\u00f3s de metal nu quanto em GPUs. Esta flexibilidade aumenta significativamente o desempenho e facilita a concretiza\u00e7\u00e3o dos objetivos de investiga\u00e7\u00e3o de forma perfeita. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Adaptabilidade.<\/mark><\/em>  Kristina Kapanova, Chefe da Nuvem de ML, elogia o FishOS pela sua adaptabilidade. <\/li>\n<\/ul>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><em>\u201cOferece uma ampla gama de op\u00e7\u00f5es de servi\u00e7o adaptadas a necessidades espec\u00edficas: seja para gerir cargas de trabalho menores ou maiores, publicar conjuntos de dados ou realizar benchmarks. Para mim, tamb\u00e9m \u00e9 um resultado positivo, pois podemos gerir facilmente as mudan\u00e7as atrav\u00e9s do FishOS.\u201d<\/em>, diz a Sra. Kapanova.<\/p>\n<\/blockquote>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Estabilidade.<\/mark><\/em>  Outra vantagem crucial da plataforma que se alinha perfeitamente com os requisitos da universidade \u00e9 a estabilidade geral do sistema.<\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Economia de Tempo.<\/mark><\/em> Al\u00e9m disso, o tempo consider\u00e1vel poupado pelo FishOS para outras tarefas e prop\u00f3sitos \u00e9 de extrema import\u00e2ncia, pois todas as modifica\u00e7\u00f5es necess\u00e1rias podem ser executadas com alguns comandos. Por exemplo, em apenas 3-4 passos simples, os especialistas do ML Cloud podem rapidamente configurar um sistema totalmente funcional para os utilizadores. O aspecto crucial aqui reside na efic\u00e1cia e rapidez com que esta tarefa \u00e9 realizada, atendendo \u00e0s crescentes exig\u00eancias dos utilizadores que est\u00e3o sempre \u00e0 procura de mais.   <\/li>\n<\/ul>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><br\/><em>\u201cConsegui entregar um servi\u00e7o aos utilizadores em uma semana que provavelmente nos teria levado um m\u00eas. A r\u00e1pida disponibiliza\u00e7\u00e3o da funcionalidade para os utilizadores e a consequente facilidade na minha carga de trabalho s\u00e3o inestim\u00e1veis,\u201d<\/em> destaca a Sra. Kapanova. <\/p>\n<\/blockquote>\n\n<p class=\"wp-block-paragraph\"><br\/>Ao abordar as necessidades dos utilizadores, o sistema de tickets do ML Cloud serve como a principal ferramenta de comunica\u00e7\u00e3o interna da organiza\u00e7\u00e3o, desempenhando um papel vital na resolu\u00e7\u00e3o r\u00e1pida de quaisquer problemas emergentes. O seu funcionamento suave \u00e9 tamb\u00e9m creditado ao FishOS. <\/p>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Gest\u00e3o de Rede.<\/mark><\/em> Al\u00e9m da estabilidade, a gest\u00e3o de rede \u00e9 uma prioridade. A plataforma supervisiona todo o sistema de forma integrada, atribuindo e gerindo endere\u00e7os IP, aliviando os especialistas de outra responsabilidade pesada e permitindo-lhes focar em tarefas de n\u00edvel superior. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Sem Interrup\u00e7\u00f5es.<\/mark><\/em> Kristina Kapanova observa que n\u00e3o h\u00e1 interrup\u00e7\u00f5es no uso da plataforma. Embora os especialistas do ML Cloud possam ocasionalmente encontrar problemas de hardware, uma vez resolvidos, a infraestrutura de cloud volta a funcionar em poucos minutos. <em>&#8220;Foi a primeira vez que experimentei um retorno ao servi\u00e7o t\u00e3o suave,&#8221; <\/em> observa a Sra. Kapanova. <\/li>\n<\/ul>\n\n<ul class=\"wp-block-list\">\n<li><em><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">Suporte.<\/mark><\/em> Uma vantagem ineg\u00e1vel do FishOS \u00e9 o seu suporte incrivelmente r\u00e1pido e fi\u00e1vel fornecido \u00e0 universidade. Desde o in\u00edcio da ado\u00e7\u00e3o da plataforma pelo ML Cloud, a equipa t\u00e9cnica da Sardina Systems ofereceu orienta\u00e7\u00f5es detalhadas. Isto permitiu que os especialistas, mesmo aqueles n\u00e3o familiarizados com opera\u00e7\u00f5es rotineiras, rapidamente as dominassem.   <\/li>\n<\/ul>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"576\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png\" alt=\"\" class=\"wp-image-66194\" srcset=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1024x576.png 1024w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-300x169.png 300w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-768x432.png 768w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1536x864.png 1536w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1390x782.png 1390w, https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/ml-case-4-quote-kristina-wo-headshot-1920x1080.png 1920w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\"><br\/>Quando v\u00e1rias m\u00e1quinas virtuais experimentaram uma desacelera\u00e7\u00e3o s\u00fabita durante o L2L Challenge, uma das competi\u00e7\u00f5es de aprendizagem autom\u00e1tica mais significativas para cientistas, o problema de insufici\u00eancia de RAM foi rapidamente identificado e resolvido em poucos dias. Os esfor\u00e7os eficazes e bem coordenados dos especialistas da Sardina Systems foram fundamentais para prevenir incidentes semelhantes no futuro. <em>&#8220;Considerando o seu excelente suporte, funcionalidade e benef\u00edcios de economia de tempo, bem como a disponibilidade cont\u00ednua de todos os sistemas, o FishOS parece oferecer uma excelente rela\u00e7\u00e3o qualidade-pre\u00e7o,&#8221; <\/em> acrescenta a Sra. Kapanova. <\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">O Que Vem a Seguir: O Curso de Inova\u00e7\u00e3o e Crescimento de T\u00fcbingen<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">Em termos de planos futuros,<strong> o Centro de IA dentro do ML Cloud est\u00e1 definido para dobrar de tamanho <\/strong> nos pr\u00f3ximos anos. Consequentemente, o ML Cloud est\u00e1 a expandir a sua infraestrutura <strong> para atender \u00e0s crescentes demandas de computa\u00e7\u00e3o <\/strong>. A universidade pretende ampliar as suas capacidades incorporando <strong> processadores n\u00e3o-GPU para cargas de trabalho espec\u00edficas de IA e ML <\/strong> em diferentes \u00e1reas. Esta iniciativa visa <strong> desenvolver solu\u00e7\u00f5es personalizadas para reduzir o consumo de energia enquanto mant\u00e9m o desempenho. <\/strong>    <\/p>\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p class=\"wp-block-paragraph\"><br\/><em>&#8220;Estou encantada que a Sardina Systems tenha testado o FishOS para <\/em><a href=\"https:\/\/www.sardinasystems.com\/pt-pt\/noticias\/a-sardina-systems-anuncia-uma-compatibilidade-perfeita-com-os-novos-processadores-gaudi2\/\"><em>compatibilidade com Gaudi2<\/em><\/a><em>, e os benchmarks iniciais parecem promissores. Al\u00e9m disso, planeio usar FPGAs com a plataforma para oferecer mais op\u00e7\u00f5es aos utilizadores. A oportunidade de implementa\u00e7\u00f5es bare-metal com estes n\u00f3s \u00e9 particularmente gratificante,&#8221;  <\/em> comenta a Sra. Kapanova.<\/p>\n<\/blockquote>\n\n<p class=\"wp-block-paragraph\">Al\u00e9m disso, o ML Cloud est\u00e1 a aumentar a sua utiliza\u00e7\u00e3o do armazenamento Ceph, e a Sardina Systems fornecer\u00e1 suporte para isso, dado que o FishOS \u00e9 constru\u00eddo com base na tecnologia Ceph.<\/p>\n\n<p class=\"has-medium-font-size wp-block-paragraph\"><strong><mark style=\"background-color:rgba(0, 0, 0, 0)\" class=\"has-inline-color has-primary-1-color\">O Pr\u00f3ximo Passo para o Sucesso do Machine Learning Cloud<\/mark><\/strong><\/p>\n\n<p class=\"wp-block-paragraph\">O FishOS destaca-se como um divisor de \u00e1guas para institui\u00e7\u00f5es acad\u00e9micas. Simplificando fluxos de trabalho e libertando os investigadores das complexidades t\u00e9cnicas, capacita universidades como T\u00fcbingen a inovar sem problemas enquanto otimiza as atividades operacionais. <\/p>\n\n<figure class=\"wp-block-image size-large\"><a href=\"https:\/\/www.sardinasystems.com\/pt-pt\/sobre-nos\/contato\/\"><img decoding=\"async\" src=\"https:\/\/www.sardinasystems.com\/wp-content\/uploads\/2024\/12\/Banner&#x2013;CTA-offer-1024x231.png\" alt=\"\" class=\"wp-image-66203\"\/><\/a><\/figure>\n\n<p class=\"wp-block-paragraph\"><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Durante quase dois anos de estreita colabora\u00e7\u00e3o com um dos principais centros de investiga\u00e7\u00e3o da Alemanha, a Universidade de T\u00fcbingen, a Sardina Systems forneceu suporte essencial para melhorar a efici\u00eancia e produtividade da infraestrutura de nuvem de machine learning da universidade. Neste estudo de caso, vamos explorar como o FishOS, a plataforma de gest\u00e3o de [&hellip;]<\/p>\n","protected":false},"author":20,"featured_media":66291,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1257],"tags":[1349,1511,3189,3190,3191],"class_list":["post-66240","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-o-nosso-blog","tag-infraestrutura-informatica","tag-sistemas-de-sardinhas-em-nuvem","tag-ai-cloud-pt-pt","tag-cloud-for-researchers-pt-pt","tag-machine-learning-cloud-pt-pt","grve-entry-item","grve-blog-item"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.3 - aioseo.com -->\n\t<meta name=\"description\" content=\"Discover how FishOS powers AI and Machine Learning cloud, enabling efficient and successful scientific research for the University of T\u00fcbingen.\" \/>\n\t<meta name=\"robots\" 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